{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "##解决图例中文乱码，设置参数\n",
    "matplotlib.rcParams['font.family']=['sans-serif']\n",
    "matplotlib.rcParams['font.sans-serif']=['Songti SC']#显示中文\n",
    "matplotlib.rcParams['font.serif']=['Songti SC']\n",
    "matplotlib.rcParams['axes.unicode_minus']=False #正常显示符号\n",
    "from math import pi\n",
    "\n",
    "#屏蔽警告信息\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 双Y轴图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dfL3+IGUK52FMx3CaVLVnAYoEkEUkgMR/pKSYJc4zZsB770GfPnZXlDEnTkDr1qbv3Ny5cK/Lt1gRbrJqdywD50Rx7MIVujaqQN97qpMvl33NayWALCIBJP4lNRW6doVvv4UxY6B/f7srypzTp+Huu03n7cWLZSTk5c5dTuKdBTuY/ddRqhTLz9hO4dQtX9jusiSArCIBJP5Ha3juOZg+3Sy5fustuyvKmnPnzHmgkydh0ybT/FR4Fa01CyNPMPSXKM7HJ/NSi8q8fFcVcuX0jDZMEkAWkQAS/zNhgum1NmgQvPOO3dVkT3S0WRFXqhSsX+8Zy8ZFhpyMS2Dw3CiW7jhJWOlgxnUOp2ZJF3fayCQJIItIAAnAdDho3RoefBBmzfKspdZZtWwZtGkDDzxgVvIFuHzXY5ENWmt+2nyYEQt2kpTi4PW7q/Fsk4rktKB5qNUkgCwiASQ4cgRuvx2KFIGNG13f182dJk6EV1/1jVGdDzt0Jp4350Twe/QZ6lcswthO4VS8xXP/vntjAMl+w8LzJCaanmpXrsCcOb4VPmAunN22zWzlEBZmLqoVHiPVofnyjwO8t2Q3OQIUI9qH8lj9cpY3DxUSQMIT9eplRj2zZ0PNmnZXYz2lzJ5Cu3aZ1X1Vq0rLHg+x9+RF+s2K4O9D52lZPYSRHcIoVSiP3WX5LJmCE55l6lTo3t3s5TNqlN3VuNbJk1CvngmkTZvM5nnCFkkpDiav3sfHK6LJlysHQ9vV5qFbS7m0eajVvHEKTgJIeI4NG8zmbi1awMKF/rHL6F9/QZMm5nUvWuQbCy28TMSR8/SbGcGuExdpV6cUQ9vV4pb8rm8eajUJIItIAPmhM2fg1lshZ06zlXbRonZX5D6TJpmedp9+arYOF26RkJzKhN/2MHVtDCEFcjGifRh31/LeUagEkEUkgPzQ44+b/Xw2bDCr3/yJw2GWZv/+u1mcUKWK3RX5vA0xZ+g/K4IDZ+J5tH5ZBrStSXAe9zYPtZoEkEUkgPzMnDnQsaPZWG7IELursceRI2ZFXM2aZmdVf5h+tMHFhGTGLNrFdxsOUa5IXsZ0DKNRFd/YvVYCyCISQH7k9GmoXRtKlzajn0DvfheaLd9/b0aCo0aZRRjCUit3xfLWnEhOxiXQrXFFXr+nGnmDfGchsASQRSSA/Eja3j6bN0N4uN3V2Etr+L//M12zN24058REtp29nMTb87czd+sxqhbLz7jO4dxWzv7moVaTALKIBJCfmDkTunQxF2QOHGh3NZ7hzBkIDYVbbjGhnMv7VmN5Cq01v0YcZ9gv27lwJZmXW1bhpZaVPaZ5qNUkgCwiAeQHYmPN1Fv58vDnn2b1mzAWLoT774d+/WDsWLur8UonLiQwaG4Uy3aeJLyMaR5ao4SPddS4igSQRSSA/ECXLvDLL7Bli3nHL/7thRfMRbmrV0PTpnZX4zW01szYdJhRC3aS7HDQ5+7qPNO4gkc2D7WaBJBFJIB83E8/mXMdo0fDgAF2V+OZLl2COnXMarjISJmKy4CDZy4zYFYk62PO0KBSEcZ0DKeCBzcPtZoEkEUkgHxYbCzUqgWVK5vrXmTq7fqWLDHXB3nzRnxukOrQfPH7ft5bupvAgADeur8m/1evrN81D5UAsogEkA/r2tUsN9661QSRuLHOnc05oR07oEIFu6vxOLtPXKTfzG1sO3KB1jWLMaJ9GCWCc9tdli0kgCwiAeSj1q+HRo3MtNvo0XZX4x0OH4YaNeCee8wFuwIwzUMnrYzmk1XRFMgdyNB2tXiwjnc1D7WaBJBFJIB8UGqq2Y765EmzDUH+/HZX5D3GjjWhvWAB3Hef3dXYbuvh8/SbuY09Jy/R/tZSDGlXmyL5guwuy3Y3CyClVFngLWAbUBsYAwwEYoEorfVMtxSaviYJIOEWU6aYlV3ffw+PPmp3Nd4lKcksSEhKgqgoyOOf+9NcSUpl/NLdTP99P8UL5mZkh1DuquG9zUOtloEA6gOkaK0/VEr9CBQCZgDfASeAylrrc24p1sn31yYK+509a06iN2tmOh+IzAkKgo8/hpgYGDfO7mps8ce+09z7wRqmrdvPo/XLsbR3Mwmf/8qplNqc7qP7Vd//CWiolGoA/AzUA05orZOAFKCRm+t1z46o1xj6vaI9ceglXGPwYDh3Dj76SPa7yapWrf5Zuv7EE2YVoR+IS0hm9MKd/LDxMBWK5mVG9wY0qORHW3VkTorWut4Nvp8f+BQ4CEwFlPMjjdsHJO464MPALq31ZKAYZugn/MHWrTB5Mrz8svR6y67x402z1ldeMX3jfNyyHSe5+/3V/LjpMC80q8SiV5tJ+GTPi0B+rfVxYDsmkMoopYKAQGC9uwtyyzkg5wjoXeADoMzNTnbJOSAfobW5in/3btizBwr7XgNItxs/Hvr2NQ1LH3rI7mpc4sylRIbN38H8bceoUaIA4zqHE16mkN1lebwMnAO6HegJLAUaAu8Dg4EjwE6t9Y9uKTR9TW4KoJqYkc8ezIvvoLWOvuo+3YHuAEFBQXUTExNdXpdwsW+/hSefhGnT4Nln7a7GNyQnw223mU4Ju3ZBbt+55kVrzbytxxg+fzuXElPodVdVejSvTFBOOVWdEbIM+3oHUWoisERrvUApNQOYobWee737ywjIB8TFQfXqULasaTYaIH9ELLN8ObRuDe+9B3362F2NJY6dv8KguVGs2BXLbeUKMbZTONWKF7C7LK/ijQHkrj4oXwI9lVIFgJPAQjcdV9hl1Cg4cQLmzZPwsVqrVv+06OnWzaunNh0OzfcbDzFm0S5SHZohD9Ti6UYVyOFnbXT8lVwHJKx35AhUrWrayHzzjd3V+KaICLNhXd++Xrs0e//pywyYFcGG/WdpXKUoozuEU65oXrvL8lreOAKSABLWe+45Ezy7d0v/Mld65hn44Qfz37l8eburybCUVAfT1u1nwm97CMoZwOD7a9GlXhm/bqNjBQkgi0gAebEdOyAszCwVnjDB7mp82+HDUK2a2Vvp66/triZDdhyLo/+sCCKPXuDuWsUZ0T6U4gV9ZyGFnSSALCIB5MXat4cVK8xV+7fcYnc1vu/NN02vuC1bzOo4D5WYksrHK6L5dNU+CuUNZPiDodwXVkJGPRaSALKIBJCX+v13aNIERoyAgQPtrsY/nD9vuiLUrQtLl9pdzTVtOXiO/rMiiI69RMfbSjP4gVoUluahlpMAsogEkBfS2vR6i442H/m86vfAu33wAfTubTawu+ceu6v5n/ikFN5dspsv/zhAyYK5GdkxjJbVi9ldls+SALKIBJAXmj8fHnwQPv0UevSwuxr/kpgINWtCwYLw118esex93d7TDJgdwZFzV3iqYXn6talB/lyy+60rSQBZRALIy6Smmj5vycmwfbvpVybca8YMs83F11+b7hM2uXAlmZELdvDT5iNUvCUfYzuFU79iEdvq8ScSQBaRAPIyX3xhLoj8+Wdz7Y9wP4cD7rzTbPi3Z48tLXqWbD/B4LlRnLmcRPdmlXi1VVVyB+Zwex3+SgLIIhJAXuTKFbMUuFQp03JHVjXZZ8UK0yXhww/NMng3OXUxkWG/bGdB5HFqlizIuE7hhJUJdtvxhSEBZBEJIC/y3nvwxhuwciW0aGF3NeKuu2DnTti3D/K6tquA1po5fx/l7V93EJ+YyiutqvBC88oE5rD/HJQ/kgCyiASQl7h40XQ6uOMOWLzY7moEwNq1ZjXi+PHw+usuO8zR81cYOCeSVbtPUbd8YcZ2CqNKMWkeaicJIItIAHmJ0aPNVtsbNkD9+nZXI9Lcc4/ZCHD/fsuXwzscmu82HGTMol1ooN+91XmqYQUCpHmo7SSALCIB5AXi4qBiRWjQABYssLsakd769dCokemQ0K+fZU+779Ql3pwVycYDZ2la9RZGdQijbBFpHuopJIAsIgHkBUaNMt0ONm40U3DCs9x3n/l/s38/FMje1FhKqoMpa2P4YNlecucMYNADtehSV5qHehoJIItIAHm4uDhz7qdxY3MBqvA8mzaZadGRI800aRZtP3aB/rMiiDoaR5vaJXi7fW2KFZDmoZ5IAsgiEkAebuRIGDTI/JGrV8/uasT1PPggrFtnRkHBmVsWnZCcykcr9jJ5dQyF8wbxzkO1aRtW0kWFCitIAFlEAsiDXbhgzv00aQK//GJ3NeJG/v4bbr8dhg+HIUMy/LDNB87Sb1YEMacu07luGQbdX5NCeaV5qKeTALKIBJAHe+cd88dsyxbzx014to4dzQWq+/ffdOvuS4kpvLt4F1//eZBSwXkY3TGMZtVC3FSoyC4JIItIAHmoCxfMuZ9mzWDePLurERkREQF16sDgwfD229e92+o9p3hrdiTHLlzh6YYVeOPe6uST5qFeRQLIIhJAHurtt2HoUNNx2YM3PxNXefhhc6Hw/v1QtOi/vnU+Pol3ft3JrL+OUDnENA+tV0Gah3ojCSCLSAB5oPPnzbmfFi1gzhy7qxGZsX272SZ94EAzheq0KPI4g+dt51x8Ei82r0zPu6pI81AvJgFkEQkgDzR8OAwbZk5s33qr3dWIzOrcGZYtg4MHiVW5GDJvO4u3n6B2qYKM6xxO7VLSPNTbSQBZRALIw1y4AOXLm0aXs2fbXY3ICueKuMiX+vF4yF0kpDjo3boazzetSE5pHuoTvDGA5CdP3Nwnn5gQGjzY7kpEFh0uX52tYY0o9cVk6hQOZPGrTXmxRWUJH2Er+ekTNxYfDxMmQNu2svDACzkcmi9/38+9H6zh3bqdKHoljq9St1IpJL/dpQkhASRuYto0OHUqW+1chD2iYy/S5bP1DJu/gzsqFGHchBehZUsCxo+HhAS7yxNCzgGJG0hKgsqVoVIlWL3a7mpEBiWnOpiyJoYPl+0lb64cDHmgFh1uK22ahy5fDq1bw6efQo8edpcqLOSN54AkgMT1TZsGzz8PS5aYPWaEx4s6eoF+MyPYcTyO+8NLMqxdbUIK5PrnDlqbrRpOnIA9eyAw0L5ihaUkgCwiAeQBUlKgRg0oVMg0HZXW+x4tITmVD5fvZcqaGIrkC2JE+1DurV3i2nf+9Vdo1w6+/BKeftqtdQrXkQCyiASQB/jhB3jsMbPsukMHu6sRN7Bx/1kGzIog5vRlHq5XhoH31SI47w1GNlqbBSUJCeYi1Rxy8akvyGgAKaWeAhK01j+5oawb1yIBJP7D4TD9wxwOiIyEAFmr4okuJiQzbvFuvvnzIGUK52FMx3CaVL0lYw/++WfToufHH82/wutlJICUUpOAv7TWnyulSgMDgVggSms90x11/qseCSDxH/PmQfv28M038MQTdlcjrmHl7lgGzo7keFwCzzSqSN97q5E3KBPNQ1NToXZtyJULtm6VKVYfcLMAUko1BCZrres4v54BLAG+A04AlbXW59xSrJNb39oqpZ5SSsnbLU+mtdluu2JFeOQRu6sRVzl3OYnXf9zKM19sIl+unMx6sRFD2tXKXPiAmXZ76y3TLfvXX11TrPA07YGVSqlHlVJDgXuBE1rrJCAFaOTugtwWQM6hX6AnzDuKG1i+HDZuhP79Iae04/cUWmt+jThG6/dX88u2Y7xyVxV+faUJt5e78R4/N/Too2Z7jVGjzBsP4e1yKqU2p/voftX3g4E4rfUPQEPnbemHvm6fa3fLXxjn0K+J1vpldxxPZMPIkVCyJHTtanclwulkXAKD50axdMdJwkoH8+1zd1KzZMHsP3FgIPTtCz17wtq1Zp8n4c1StNb1bvD9HUBt5+cXMaOeMkqpICAQWO/i+v7DLeeAlFJjgVzABqAaMNo57Et/n+5Ad4CgoKC6iYmJLq9LXGX9enONyPjx8Prrdlfj97TW/LT5MCMW7CQpxUGfe6rRrbHFzUPj480o6I47YMEC655XuF0GzgEVAD4BVgFNgGHAYOAIsFNr/aMbyvx3TW4KoMlArNZ6iFJqMTBLaz31eveXRQg2ad8e1qyBQ4cgv/QKs9OhM/G8OSeC36PPcGfFIoztFE6FW1x0icfIkTBoEGzbBuHhrjmGcDlvvA7IXXN+O4Dizs8vAg43HVdk1M6dZvVbz54SPjZKdWg+X2eah247fIER7UP54fkGrgsfgJdeMv/Px4513TGEuAZ3jYCuHvr10Fpfd45NRkA2eOYZc03IwYMQEmJ3NX5pz8mL9JsZwdbD57mrRjFGdgilZHAe9xy8b1/T9Tw62qyAFF7HG0dAch2QgCNHTMPRF16Ajz6yuxq/k5TiYPLqfXy0Yi/5c+Vk2IO1ebBOKdM81F2OHjXB0707fPyx+44rLCMBZBEJIDfr0wc+/NC8+61Qwe5q/Mq2w+fpPyuCXScu0q5OKYa1q0XR/Llu/kBXeO45+O47MwouVsyeGkSWeWMASY8Vf3f2LEyZYi46lfBxmytJqYxeuJMOn/zOufgkpj5Vj48evc2+8AF44w1ITISJE+2rQXgdpVSYs61PpmU6gJRS1bJyIOGhPvkELl2Cfv3srsRv/BlzhrYfruGzNTH83x1l+e315txdq/jNH+hq1atDx44waRLExdldjfBQSqnhSqlzSqlxzpsqAIey9Fw3moJzdi+4+ixoDa21S1s2yBScm1y5AuXLyzUgbnIxIZkxi3bx3YZDlCuSlzEdw2hUJYPNQ91l0yaoXx/efdcsTBBew11TcEqpQ0AnoAxQFPgCSNZaZ3pAc7NOCDuAHMCFzD6x8AJffGG22+7f3+5KfN6KXScZOCeKk3EJPNekIn3uqU6eIA/cBuGOO+Cuu8yKuF69TLNSIf7tL631JmCTUqo20BjI0mKCm42AFKaFztp0t5XQWp/IysEySkZAbpCSAtWqQfHi8Mcf0g3ZRc5cSuTtX3cwb+sxqhXPz9hO4dyWnf5t7vDbb2YH3GnT4Nln7a5GZJAbR0BhQDWt9Szn11WBj7TWbTL9XLIKzk+lbTg3dy489JDd1fgcrTXzI44z7JftXExI5qUWVXi5ZRWCcnrBuh+toV49c25w507ZD8pLeOMqOAkgf5S2I2ZSEkRFyR8Yi524kMCguZEs2xlLnTLBjO0cTo0SFjQPdacffzQrI2VHXK8hAWQRCSAXW7IE2rSB6dNNBwRhCYdDM2PTYUYv3Emyw0Hfe6rzTOOK5AjwwunNlBSzKi4kxDSplSlaj+eNAZSht75Kqb5KqTil1AGl1H2uLkq42LhxULo0PP643ZX4jAOnL/PYtD95a04koaWDWfJaM55rWsk7wwfMXlB9+sCGDbBund3VCA9kRS5kaASklNoLDAfiMH3cXBpCMgJyoc2bzUqn994zf2BEtqQ6NNPX7Wf8b7sJDAjgrftr8sgdZd3bRsdV0pbp168vu6Z6AXePgKzIhYxuSPeR1vpb56q4250Hf1pr/VVmDyhsNm4cBAfD88/bXYnX233iIv1mbmPbkQu0rlmMEe3DKBGc2+6yrJMnj1mKPWSIOVcYGmp3RcKzZDsXbjoCUkpNAZ7jn3XeaW/ttNbaJRcyyAjIRaKjzbx+//5mG2aRJUkpDiatjOaTVdEUzB3IsAdr80B4Sd8Y9VztzBkoVw46d4av5P2mJ3PnCMiqXMjICKgX8AewMv3xgQYZPYjwEOPHm7n9V16xuxKvtfXwefrN3Maek5dof2sphrSrTZF8QXaX5TpFi5rR8qRJMGIElC1rd0XCM1iSCxkZARUF7k93U1Wt9eDMHCSzZATkAidPmvn8p54yzUdFplxJSmX80t1M/30/xQvmZmSHUO6q4QH929zh4EGoXBlefdW8iREeyc0jIEtyISMjoCrAK5idTBWym6l3+ugjc92P9PfKtD/2nWbArEgOnY3niQbl6N+mBgVyB9pdlvuUL2+uCZoyxWzdXdjDOzkId7AkFzKyDPsI0EJr3VJr3QKIycqBhI0uXTJTKB06mPY7IkPiEpJ5c3YEj03dQICCGd0bMKJ9mH+FT5p+/czP0aef2l2J8AyW5EJGRkAFgW+cJ1hzA5WzciBho2nT4Px52XIhE5btOMnAuZGcupjIC80r0bt1NXIHemDzUHcJDzcXL3/4Ibz+OuT2odV+IissyYWMnAO6E2gKnAJSgY1a6z1ZOVhGyTkgCyUnm/n7SpVg1Sq7q/F4py8lMnz+DuZvO0aNEgUY1zmc8DKF7C7LM6xcaTplT55stm8XHsXN54AsyYWMXohaArPqQQPva63PZvZAmSEBZKFvvjELDxYsgPukicX1aK2Zt/UYw+dv53JiKr3uqsILzSt7R/NQd9Ea7rwTzp2DXbsghx+PCD2QDReiZjsXMhpAi4AU54G01tql7ZMlgCyitZk6AYiIkH5e13Hs/BUGzY1ixa5YbitXiHGdwqlavIDdZXmmmTOhSxf4+WdzbZDwGDYEULZzIaOdEFZord91HvT1zB5E2GTRInMF+9dfS/hcg8Oh+X7jIcYs2kWqQzP4gVp0bVTBe/u3uUOHDlC1KowZA506yc+Vf8t2LmRmBLQbk3S1tdb3ZOVgGSUjIIs0bw4xMeYj0A9Xbt3A/tOX6T8rgo37z9K4SlFGdwinXNG8dpflHaZOhe7dYdkyaNXK7mqEk00joGzlQkYnuPsAJYBSQO/MHkTYYP16WLPGNByV8PmflFQHk1fvo80Ha9h5PI5xncL59tk7JXwy48knoUQJGDvW7kqEvbKdCxkdAU0FpmmtN2TlIJklIyALtG8Pa9eaq9jz57e7Go+w41gc/WdFEHn0AvfUKs477UMpXlCWE2fJ2LEwYABs2QK33253NQJbRkDZzoWMjoBKAPmUUuWUUo9k9WDCTXbuhHnzoGdPCR8gMcW00Xnw43Ucv3CFSY/dzmdP1pXwyY4ePaBgQdNdXfirbOdCRkdA84B2zi9d1gU7jYyAsumZZ8yWyocOwS232F2NrbYcPEf/WRFEx16i4+2lGXx/LQr7cvNQd+rf3+wrtWePudZM2MqGEVC2cyEjF6IGALcCl4EEIC9wQGt9JbMHyygJoGw4fNhcdPriizBxot3V2CY+KYV3l+zmyz8OUCo4DyM7hNKiejG7y/Itx49DhQrQrZu06PEAbr4Q1ZJcyEgAKWAfUD7dzcnAi1rrLzJzsIySAMqG1183wbNvn2ki6YfW7T3NgNkRHDl3hScblKd/2xrkz5XRKw5EpnTvbpb5HzwIxf2kO7iHcnMAWZILNz0HpE1C/QYUAqpitmAt4vxceJKzZ03H4kcf9cvwuRCfTL+Z23ji8w0E5Qjgpxca8k77UAkfV+rb13RZ9+PRtj+yKhcy+ptZFHgKCAaeBGYAtTJzIOEGkybB5ct+2XR0cdQJBs+L4uzlJF5sUZlXW1X17+ah7lKtGnTsCJ98YlbFFZAOEn4k27mQ0UUI1YCBQGFgGnAFKK21/jJz9WaMTMFlQXy8GfU0aADz59tdjducupjIsF+2syDyOLVKFmRc53BCSwfbXZZ/2bQJ6tc3CxL69LG7Gr+V0Sk4pVQDYBDwAubveiwQpbWemcnjZTsXMhRAVkl74VrrB250PwmgLPj4Y+jVy1z706SJ3dW4nNaa2X8d5e1fd3AlKZVXW1ele7NKBOaQ5qG2aNXKNCiNiYFcueyuxi9lJICUUmFAX8y5mxPAEuA75+eVtdbnXF5oOm77bXW+8BcBuTDFasnJ5t1n48Z+ET5HzsXz9Beb6PPzNqoUy8/CV5vycssqEj526t8fjh2Db7+1uxJ/llMptTndR/f033SOWCoCK5033Q2c0FonYZqKNnJvuRk/B5QtV73wru44pl/58UezCumjj+yuxKUcDs23Gw4ydtEuNDCsXS2ealiBAGkear+774a6dU2T0qefhpyy8MMGKVrrejf4/gDMQoFyzo8AzHbaadz+Ds4tU3BKqen8+4W/qbWeetV9ugPdAYKCguomJia6vC6f4HBAnTrm38hICPDNUcC+U5cYMCuCTQfO0bTqLYzqEEbZItK/zaPMnm06ZP/wAzwiDVPcLRPngLpiBgKxwDLgS+AkUFVrfdqFJf63FjefA+oKdHXuIX5dcg4oE+bNM33fvvkGnnjC7mosl5zqYOraGD5Ytpc8gTkY/EAtOt1eGiXbAHgehwNCQ83oZ9s22arBzbIQQI9jlk8fAXZqrX90aYHXqkUCyIul7VB55gzs3u1z0x5RRy/Qf1YE24/F0Ta0BMMfqk2xAtK/zaOl7cA7fz48cMO1RsJi7m7FYwW3BlBGSQBl0G+/wT33wGefmSvSfURCcioTl+/lszUxFM4bxDsP1aZtWEm7yxIZkZxsrg0qUQL++ENGQW4kAWQRCaAMatECoqNN2x0fWfq6+cBZ+s2KIObUZTrXLcOg+2tSKK80D/Uqn34KL70EK1ZAy5Z2V+M3JIAsIgGUAevWQdOmMGECvPaa3dVk2+XEFMYt3sXXfx6kVHAeRncMo1m1ELvLElmRkAAVK5rzQb/9Znc1fkMCyCISQBnQti1s3gwHDkA+r/qZ+4/Ve07x1uxIjl24wlMNytOvTQ3ySf827/bee/DGG7Bhg+mSIFxOAsgiEkA3sWUL1KsHI0fCW2/ZXU2WnY9P4p1fdzLrryNUDsnHuM7h1C1fxO6yhBUuXjStoZo3hzlz7K7GL3hjAMnbTG80ahQEB8PLL9tdSZYtijzO4HnbOR+fRM+WVeh5VxVpHupLChSAV16B4cMhKspMxwlxFd+8atGX7dhhLvjr2dOEkJeJvZhAj2+28OJ3f1EiOBfzejam773VJXx8Ua9eZnp4zBi7KxEeSqbgvM2TT5oAOnjQq7bb1lozc8sR3vl1BwkpDnq3rsbzTSuSU/q3+bY33oD335dtu93AG6fgJIC8SUyMucbilVfML7WXOHw2nrfmRLJ272nqVyjCmE5hVAqRnrR+4fhxsyLuySdh6tSb319kmQSQRSSAruOFF+DLL2H/fihVyu5qbirVofl6/QHeXbIbBQxoW4PH7ywvzUP9zSuvmGuD9uwxYSRcQgLIIhJA13D4MFSpAs88A5Mn213NTUXHXqT/rEi2HDxH82ohjOoYRulCeewuS9jh6FEz/SajIJeSALKIBNA1vPgifP457N1rlrd6qORUB5+t3sfE5dHkzZWDIQ/UosNt0jzU78koyOUkgCwiAXSVgwehalV49lnzS+yhIo9c4I2Z29h14iL3h5dkWLvahBTwjRZBIptkFORyEkAWkQC6Stq5n+hoKFvW7mr+IyE5lQ+W7WXq2hiK5AtiRPtQ7q1dwu6yhKeRUZBLSQBZRAIonf37zcq37t1h0iS7q/mPjfvP0n9WBPtPX+b/6pXlrftqEpw30O6yhCeSUZBLSQBZRAIoneeeg2+/NaOfMmXsruZ/LiYkM27xbr758yBlCudhTMdwmlT1nuuShE1kFOQyEkAWkQBy2rcPqlc3re0nTrS7mv9ZuTuWgbMjOR6XwDONKtL33mrkDZKuTiIDZBTkMhJAFpEAcnrmGZgxwwSRB1z3c/ZyEu/8uoM5fx+larH8jO0czu3lCttdlvA2MgpyCQkgi0gAYabcatQw/bQmTLC1FK01CyKPM3Tedi5cSealllV4uWVlcuWU/m0iC2QU5BISQBaRAAKefhp+/tm03ylh34qyk3EJDJobxW87ThJeJpixncKpWbKgbfUIHyGjIMtJAFnE7wNozx6oWRN69zYbe9lAa81Pmw8zYsFOklIc9LmnGt0aS/NQYZG0UdDjj5sLrEW2SQBZxO8D6IknzCZe+/dDsWJuP/yhM/EMmB3BH/vOcGfFIoztFE6FW7zq51p4g969zeKaqCjzhktkiwSQRfw6gHbuNJt39ekD48a59dCpDs2XfxzgvSW7yRGgePO+Gjx6RzlpHipc4/RpqFQJWrc2W4yIbJEAsohfB1D79rBihVn5FhLitsPuOXmRfjMj2Hr4PHfVKMbIDqGUDJbmocLF3nkHhgyB9euhQQO7q/FqEkAW8dsA+v13aNIERoyAgQPdcsikFAeTV+/joxV7yZ8rJ0Pb1eahW0tJ81DhHpcumXNBNWvCypUgP3dZJgFkEb8MIK1N+Ozfbzpe53P9z9G2w+fpPyuCXScu8mCdUgxtV4ui+aV5qHCzjz82lxssWgRt2thdjdeSALKIXwbQ3LnQoQNMmQLPP+/SQ11JSmXCsj1MWxtDsQK5GdE+lNa1irv0mEJcV1KSGQEVKAB//QUBstIyKySALOJ3AZSSAmFh5vPISMjpurY26/ed4c3ZERw4E8+j9cvx5n01KJhbmocKm33/vVmS/d138NhjdlfjlSSALOJ3ATRlitlyYc4cswjBBeISkhmzaBffbzhE+aJ5Gd0xjEaVpXmo8BAOB9StC3FxZiVoUJDdFXkdCSCL+FUAXb5sttquVAnWrXPJSdjlO08ycE4UsRcTeLZJRV6/uzp5gqSNjvAwixdD27bmnNDLL9tdjdeRALKIXwXQiBEweLBZAdeokaVPfeZSIsPn7+CXbceoXrwAYzuHc2vZQpYeQwjLaA0tW5oR0L59kD+/3RV5FQkgi/hNAJ06ZZagtmplpt8sorXml23HGD5/BxcTkunZsiovtqhMUE45uSs83J9/QsOG8Pbb5o2ZyDAJIIv4TQC9+qqZbrCwFcnxC1cYNCeK5btiubVsIcZ1Dqda8QKWPLcQbtGpEyxZYnoiesA2JN5CAsgifhFAMTFmu4WuXc0ihGxyODQzNh1m9MKdJDsc9L2nOs80rkgOaaMjvE1MDNSqBV26wDff2F2N17hZACmlCgCfAPWBvcDLQH8gFojSWs90S6Hpa3JHAF3jhXfSWide7/5+EUAdOsDSpeai02y+yztw+jIDZkfwZ8xZGlUuypiO4ZQrmteiQoWwwaBBMHKkWZjTuLHd1XiFDATQfcAaIBk4AWwEZgDfOb+urLU+545a/1eTmwLo6hd+l9b67+vd3+cDKG21z6hR8OabWX6alFQH03/fz/ilewjKEcDA+2vyf3eUlTY6wvtdvmy2oy9eHDZuhByyavNmMjoFp5QqiQmeUOAJrfUipVQs8IzWeoGr60zPLWeltdYLtdaXgCJABBB59X2UUt2VUpuVUptTUlLcUZY9EhNN25GqVeH117P8NLtOxNHp0z8YtXAXTauG8NvrzXmkfjkJH+Eb8uUze2H99RdMn253Nd4iZ9rfUOdH96vvoJQqBDwOtAOU8yON21cpue0ckPOFPwdM0VrH3ei+Pj0CGjXKNBpdvBjuvTfTD09MSWXSyn18sjKa4DyBDHuwNg+El5TgEb5Ha2je3CzL3rMHChe2uyKPloEpuHzAg8DPwC3ADmAA8CVwEqiqtT7thlL/qclNU3BXv/AHtNbTrnd/nw2gQ4fMwoM2bbK0/8nfh87Rf1YEe05eosNtpRn8QC2K5JMrxoUP27rVdEjo2RM+/NDuajxaBgLoTWBUupu6Ak2BI8BOrfWPrq3wGjW5KYCufuFPaa2vu7zFZwOoc2dYuNC8oytfPsMPi09KYfzSPUz/fT8lCuZmZIdQ7qohzUOFn3jpJbNSdOtWs1mjuCZZhm0Rnwyg336De+4xG3ANGpThh/0RfZoBsyM5dDaeJxqUo3+bGhSQ5qHCn5w5Y86Z3nYbLFsmewZdhwSQRXwugJKSIDzcdL2OioLcuW/6kAtXkhm9cCczNh2mQtG8jOkUToNKRd1QrBAe6JNPTH+4mTPNhariPySALOJzATR2LAwYAAsWwH333fTuv+04yaC5kZy6mMjzzSrRu3U1cgfKMlThx1JSzLmg8+dh+3bpE3cNEkAW8akAOnLELDxo1QrmzbvhXU9fSmTYL9v5NeI4NUoUYFzncMLLFHJPnUJ4ut9/h6ZNzUjoo4/srsbjSABZxGcCSGszXbBoEezYARUrXudumrlbjzJ8/g7iE1PpeVcVejSX5qFC/Merr8LEibBqlVmiLf5HAsgiPhNAabs8jhkD/ftf8y7Hzl9h4JxIVu4+xW3lCjGuUzhVpXmoENd2+bI5n6oURERAXmk5lUYCyCI+EUDHjkHt2qbL9dq1/2kl4nBovtt4iDELd+LQ8Ma91Xm6UQVpHirEzaxaZfYNeu01mDDB7mo8hgSQRbw+gLSG++83vyjbtpklpOnEnLrEgFmRbDxwliZVbmF0xzDKFpF3ckJkWM+eZmXc2rXSrNRJAsgiXh9A06bB88+bE6U9e/7v5pRUB1PX7mfCsj3kzhnAoAdq0aVuGWmjI0RmXboEYWEQGGje5OXJY3dFtpMAsohXB9CBA+YXo359c/FpgFlIsONYHP1mbSPqaBz31i7OOw+FUqzgza8HEkJcx4oVZnVpnz6mcamfkwCyiNcGkMNhfiG2bIHISChfnoTkVD5eEc3k1fsolDeQtx8KpW1oCRn1CGGFHj1g6lSzb1DDhnZXYysJIIt4bQBNnGiWiX7+OXTrxpaDZ+k3M4J9py7T6fYyDLq/JoWleagQ1omLMzMOefOarRv8eCpOAsgiXhlAu3fDrbdCq1ZcnjmHd5fu4av1BygVnIdRHcNoXi3E7gqF8E1pfRafe86MhvyUBJBFvC6AEhOhWTPYu5c/56+h77pYjpy7wtMNy/NGmxrkz5XT7gqF8G0DB5q9tpyzD/5IAsgiXhVAWpsVb59/zld9xjM0Z3UqheRjbKdw7qhQxO7qhPAPqalmn621a+GPP+D22+2uyO0kgCziVQH06afw0kt83vwxRjV6nBeaVeKVVlWleagQ7nbqlGlYmiOHWQhUxL/eAHpjAEmzsWw4t2QZqb16sbzyHcx56HnmvdyYfm1qSPgIYYeQEPj5Zzh6FJ580qxKFR5NAigLtNYsXLgJR6cuHAwuQcyEycx5pRmhpYPtLk0I/3bnnWbr7oULYcQIu6sRNyEBlElHzsXz3JR1lH72cfKkJpFj3lyeb3c7gTnkP6UQHqFHDzMCGjYMFi+2uxpxA3IOKIMcDs23Gw4yduFORvzyPh0iluGYM5eA9g/ZXZoQ4mrx8ebC1CNHYPPm626F4kvkHJCP2nfqEv83ZT1D5m1nwL5ldIhYBsOHS/gI4any5oVZs8x5oLvvNt3phceREdANJKc6mLImhg+X7yVPYA4+C4rmzsGvoNq1Mz/cAZLfQni0P/80AVS2LKxebRYq+ChvHAFJAF1H1NEL9J8VwfZjcdwXVoLRiVEEP9/NtH5fuFD2pBfCW6xeDW3bQrVqpoGpjy7P9sYAkrfwV0lITmXc4l08NOl3Yi8mMvmJ2/mEXSZ8mjaV8BHC2zRvDnPnws6d5mLVuDi7KxJOMgJKZ/OBs/SbFUHMqct0qVuGQffXInjWDOja1fwQz58P+bzqDYYQIs38+dCxIzRoYFbH+djvsjeOgCSAgEuJKby7eBdf/3mQ0oXyMLpjGE2rhsBXX8Ezz5jtf+fPl/3nhfB2P/8MjzwCLVrAr7/6VPdsbwwgv++SuXrPKd6aHcmxC1d4umEF3ri3Ovly5YQvvzRNDVu1gnnzJHyE8AVdupjmwU89ZabjfvwRSpSwuyq/5bfngM7HJ9Hnp208PX0juQMDmNmjIcMerE2+HMDQoSZ8WreGX36R8BHClzzxBHz3HWzaZJqWrltnd0VGaipER9tdhVv5XQBprVkYeZzW769m3taj9GxZhQWvNKVu+SJw8KAZmr/9trmSet48nxqiCyGcHn0UNmwwC4patIAJE0xne7tER5s6mjSBCxfsq8PN/GoKLjYugcHzoliy/SShpQvyVbf61C7l7N/200/Qvbu5cO277+Cxx+wtVgjhWmFhZhTUrRu8/rrZxuHzz6FgQffV4HDApEnQvz8EBZldld15fJv5xSIErTU/bznCiF93kJjioPfd1XiuSUVy5giAS5fMNtrTp5tGht9/D5UqWXZsIYSH0xref9+EQOXKMHOmCSdXi4kx4bd6Ndx3H0yZAqVLZ/npvHERgs9PwR0+G8+Tn2+k38wIapQoyKJXm9KjeWVyBihYssTsH/LFF2ZHxbVrJXyE8DdKQZ8+5iLVuDi47TazSGH3btccz+Ew+4iFh8Pff5s3v7/+mq3w8VY+OwJKdWi++uMA7y7ZTYCCAW1r8Pid5QlIuALffgsffGAuTCtbFr7+2sy/CiH8W2wsjB1rAiIhwSzZHjgQatfO/nPHxZlVd1OmmAap994LU6eav0EW8MYRkFsCSCkVCLwH7AFKaK0H3+j+2Q2gvScv0n9WBH8dOk+L6iGM7BBG6ctn4ZNP4LPP4MwZs/qld294+GEz9yqEEGliY8203Mcfw+XL0KmTmaKrWzdzPSC1Nqvspk8355nj46FWLejb11zgrpRlJd8sgDL7d9gd3BVAPYCGWuunlVJrgLe11suud/+sBlByqoMpv+3kl7l/UP3icZ4vkULopZOovXvN9JrDAe3bw2uvmdUmFv7PF0L4oDNnzGzJxIlmBJM/v5miq1fPhFHduqbHXEoKnDwJJ07883HwoLnwdc8e87hHHzXnfO680yV/ezIQQJn6O+wO7gqgWUC01rq/Uuon4KDW+o3r3T8rAXQpIZkzFapSOvYwOXW6rXiLFoWqVU0T0Zdf9ot9QYQQFjt/3vST27wZtmyBrVvNFB2YGZSkpGs/rkkTePZZcwGsi1v/ZCCAMvV32B3ctQxbOT/S/GcMq5TqDnQHCMrClFj+3IFE3dmMlJJFqdz4dvOupGpVn+18K4Rwo0KFzJRZ167m65QUcw5582bYsQOCg01HhfQfxYq5e3o/p1Jqc7qvp2itp6T7+qZ/h93NXSOgl4AmWuvHlFKrgTFa60XXu78nbMcghBDeJAMjoEz9HXYHdy5C+BDYAZTUWg+80f0lgIQQInMyuAghw3+H3cFnl2ELIYQ/8cZl2LbPAQohhPBPEkBCCCFsIQEkhBDCFhJAQgghbCEBJIQQwhYSQEIIIWzhkcuwlVIO4EoWH54TSLGwHG8gr9n3+dvrBXnNmZVHa+1VgwqPDKDsUEpt1lrXs7sOd5LX7Pv87fWCvGZ/4FVpKYQQwndIAAkhhLCFLwbQlJvfxefIa/Z9/vZ6QV6zz/O5c0BCCCG8gy+OgIQQQngBCSAhvIBS6iml1MN21yGElXwmgJRSgUqpD5VSLyul3rG7HndQShVQSn2jlNqtlPpVKZXL7prcQSnVQCn1q911uItSahIQqLX+ye5a3EEpVVYp9alSqodS6iOllLr5o7yXUqqVUmqN8/PaSqmJSql3lVJN7K7N1XwmgIBngUJa60lAc6VUa7sLcoOmwItAONAYqGVvOa6nlArDvOb8dtfiDkqphphdLD+3uxY3ehjYpbWeDBQDCtlbjusopWoAOYByzps+B+YAnwLfKqVy2FWbO/hSAN0NnHB+fgK418Za3EJrvVBrfQkoAkQAkTaX5FJKqWpARWCl3bW4UXtgpVLqUaXUUKVUkN0FucFPQEOlVAPgZ631ObsLchWt9S5gD5gZDeBOzN+vE0B5oIZ91bmeLwWQcn6k8aXXdl1KqULA40A7rbWvty0ZAHQDXgFClVLP21yPOwQDcVrrH4CGwNM21+MO+TEjgIPAUKVUFZvrcRd11b/g43/HfOnFLQPKOD8v7vzapyml8gFtgQ+AvEqp5+ytyLW01t201u2BiUCU1nqqzSW5ww7MzzPARcBhYy3u8iKQX2t9HNgOhNpcj1toreOATZi/Y8WBI8AuW4tyMV8KoKnAeaVUT2Cd1nqR3QW5wSvA90AycBxItLcc4QJfYN5cPAtcAr61uR53+BLopJR6BDgJLLS3HNdxTql2BAoppZoDzzi/fgl4UmudbGd9riYXogohhLCFL42AhBBCeBEJICGEELaQABJCCGELCSAhhBC2kAASQghhCwkgIYQQtpAAEsJJKdVVKbVBKdU2k4+bpZRq4/z8mq1TlFKPK6UOKaVaZL9SIXyDBJDwO0qpgkqp/Eqp3M5/A9N9eyem91oDpVRl50cDpVTBGzzlUGCdsxv5NXe01Fp/B8RY9yqE8H4SQMIf9QIuALdirrovetX3k4AngMHAOWAucLtSqotSaoVSaoJS6rAzmGoAg4C6mF51TZVSw5RStyulvldK9faTnnVCZJoEkPA7WuuRwCJM+DyntT5x1fcdwGnn52eBBOe34jDdifsCqzEd2KOBuzANJL9yPmYYcBloB1QC9rry9QjhrSSAhL9aitnGost1vp8K5HZ+ntadOBFI0lqnAilADmcH8njn9zWAUio3JqzaOB/zgdXFC+ELJICEX1FKBSilBgF5MCOgCc59Z662FrhHKTUeOAvUxkyzBSul6mBGNqHOzwsBd2D2cNkHzMeE1/P4TwNRITJNmpEKv5Nu0UGq1tqhlFJaa62U6gq00Fp3zcRzpT02wDl1d6P7rgKGaa1XZbF0IXyKjICE39FaJzs/HM6v078LC1dKPZCJ59LOf28WPo8ClbNSrxC+SkZAQgghbCEjICGEELaQABJCCGELCSAhhBC2kAASQghhCwkgIYQQtpAAEkIIYYv/B1lGUIT2c+npAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#创建数据\n",
    "x = np.linspace(0,10)\n",
    "y = np.linspace(0,10)\n",
    "z = np.sin(x/3)**2*98\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x,y, '-', label = 'group 1')\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(x,z, '-r', label = 'group 2')\n",
    "fig.legend(loc=1)\n",
    "\n",
    "ax.set_xlabel(\"x [units]\")\n",
    "ax.set_ylabel(r\"group 1\")\n",
    "ax2.set_ylabel(r\"group 2\")\n",
    "plt.title(\"双Y轴图表\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#创建数据\n",
    "x = np.linspace(0,10)\n",
    "y1 = np.linspace(0,10)\n",
    "y2= -y1\n",
    "\n",
    "#设置y轴取值范围\n",
    "plt.ylim(-10,10)\n",
    "\n",
    "#为xy轴增加标签\n",
    "plt.ylabel('Y 轴')\n",
    "plt.xlabel('X 轴')\n",
    "\n",
    "plt.plot(x,y1,label='y1数据线')\n",
    "plt.plot(x,y2,linestyle='--',color='r',label='y2数据线')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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